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    Nonlinear neural network for hemodynamic model state and input estimation using fMRI data

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    Type
    Article
    Authors
    Karam, Ayman M. cc
    Laleg-Kirati, Taous-Meriem cc
    Zayane, Chadia cc
    Kashou, Nasser H.
    KAUST Department
    Applied Mathematics and Computational Science Program
    Computational Bioscience Research Center (CBRC)
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    Date
    2014-11
    Permanent link to this record
    http://hdl.handle.net/10754/563823
    
    Metadata
    Show full item record
    Abstract
    Originally inspired by biological neural networks, artificial neural networks (ANNs) are powerful mathematical tools that can solve complex nonlinear problems such as filtering, classification, prediction and more. This paper demonstrates the first successful implementation of ANN, specifically nonlinear autoregressive with exogenous input (NARX) networks, to estimate the hemodynamic states and neural activity from simulated and measured real blood oxygenation level dependent (BOLD) signals. Blocked and event-related BOLD data are used to test the algorithm on real experiments. The proposed method is accurate and robust even in the presence of signal noise and it does not depend on sampling interval. Moreover, the structure of the NARX networks is optimized to yield the best estimate with minimal network architecture. The results of the estimated neural activity are also discussed in terms of their potential use.
    Citation
    Karam, A. M., Laleg-Kirati, T. M., Zayane, C., & Kashou, N. H. (2014). Nonlinear neural network for hemodynamic model state and input estimation using fMRI data. Biomedical Signal Processing and Control, 14, 240–247. doi:10.1016/j.bspc.2014.07.004
    Sponsors
    Research reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST). The authors would like to thank anonymous reviewers for valuables comments on the manuscript.
    Publisher
    Elsevier BV
    Journal
    Biomedical Signal Processing and Control
    DOI
    10.1016/j.bspc.2014.07.004
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.bspc.2014.07.004
    Scopus Count
    Collections
    Articles; Applied Mathematics and Computational Science Program; Electrical Engineering Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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